Optimization-Based Available-To-Promise with Multi-Stage Resource Availability

Increasingly, customer service, rapid response to customer requirements, and flexibility to handle uncertainties in both demand and supply are becoming strategic differentiators in the marketplace. Organizations that want to achieve these benchmarks require sophisticated approaches to conduct order promising and fulfillment, especially in today’s high-mix low-volume production environment. Motivated by these challenges, the Available-to-Promise (ATP) function has migrated from a set of availability records in a Master Production Schedule (MPS) toward an advanced real-time decision support system to enhance decision responsiveness and quality in Assembly To Order (ATO) or Configuration To Order (CTO) environments. Advanced ATP models and systems must directly link customer orders with various forms of available resources, including both material and production capacity. In this paper, we describe a set of enhancements carried out to adapt previously published mixed-integer-programming (MIP) models to the specific requirements posed by an electronic product supply chain within Toshiba Corporation. This model can provide individual order delivery quantities and due dates, together with production schedules, for a batch of customer orders that arrive within a predefined batching interval. The model considers multi-resource availability including manufacturing orders, production capability and production capacity. In addition, the model also takes into account a variety of realistic order promising issues such as order splitting, model decomposition and resource expediting and de-expediting. We conclude this paper with comparison of our model execution results vs. actual historical performance of systems currently in place.     

Coordination of a Supply Chain with One-Manufacturer and Two-Retailers Under Demand Promotion and Disruption Management Decisions

Increasingly, customer service, rapid response to customer requirements, and flexibility to handle uncertainties in both demand and supply are becoming strategic differentiators in the marketplace. Organizations that want to achieve these benchmarks require sophisticated approaches to conduct order promising and fulfillment, especially in today’s high-mix low-volume production environment. Motivated by these challenges, the Available-to-Promise (ATP) function has migrated from a set of availability records in a Master Production Schedule (MPS) toward an advanced real-time decision support system to enhance decision responsiveness and quality in Assembly To Order (ATO) or Configuration To Order (CTO) environments. Advanced ATP models and systems must directly link customer orders with various forms of available resources, including both material and production capacity. In this paper, we describe a set of enhancements carried out to adapt previously published mixed-integer-programming (MIP) models to the specific requirements posed by an electronic product supply chain within Toshiba Corporation. This model can provide individual order delivery quantities and due dates, together with production schedules, for a batch of customer orders that arrive within a predefined batching interval. The model considers multi-resource availability including manufacturing orders, production capability and production capacity. In addition, the model also takes into account a variety of realistic order promising issues such as order splitting, model decomposition and resource expediting and de-expediting. We conclude this paper with comparison of our model execution results vs. actual historical performance of systems currently in place.     

Formation of a strategic manufacturing and distribution network with transfer prices

We propose a profit maximization model for the decision support system of a firm that wishes to establish or rationalize a multinational manufacturing and distribution network to produce and deliver finished goods from sources to consumers. The model simultaneously evaluates all traditional location factors in a manufacturing and distribution network design problem and sets intra-firm transfer prices that take account of tax and exchange rate differentials between countries. Utilizing the generalized Benders decomposition approach, we exploit the partition between the product flow and the cash allocation (i.e., the pricing and revenue assignment) decisions in the supply chain to find near optimal model solutions. Our proposed profit maximizing strategic planning model produces intuitive results. We offer computational experiments to illustrate the potential valuable guidance the model can provide to a firm's supply chain design strategic planning process.     

A framework for evaluating remote diagnostics investment decisions for semiconductor equipment suppliers

With advances in information technology, service activities for expensive equipment used in semiconductor manufacturing can be performed from a remote location. This capability is called remote diagnostics (RD). Currently, there are intense development efforts in the semiconductor industry for implementing RD in wafer fabrication facilities to reduce maintenance and capital costs and improve productivity. In this paper, we develop a queueing-location model to analyze the capacity and location problem of after sales service providers, considering the effects of RD technology. Our model optimizes the location, capacity and the type of service centers while taking congestion effects into consideration. We solve this model using a simulation optimization approach in which we use a genetic algorithm to search the solution space. We demonstrate how our methodology can be used in strategic investment planning regarding the adoption of RD technology and service center siting through a realistic case study.     

Integrating effective flexibility measures into a strategic supply chain planning model

This paper develops models for capacity, product mix, distribution and input supply flexibility and integrates them in a strategic level, mixed integer supply chain (SC) planning model as a way of addressing demand and supply uncertainty, as well as improving market responsiveness. Capacity flexibility is modeled via the SC’s production capacity planning to address budgeted demand and ensure the fulfillment of prospective demand increases when considering various market scenarios. This model selects an optimal number of products from fast moving and extended product range options—based on the product mix flexibility. The model confirms a quick response to a changing marketplace by considering elements like transportation and supply lead time along with the probabilities of stock out options when addressing input supply and distribution flexibility. This paper proposes a solution procedure to solve the model for real world problems, and investigates the sensitivity of the model outputs with respect to changes in flexibility measures.     

Defence firms facing liberalization: innovation and export in an agent-based model of the defence industry

The paper presents an agent-based simulation model of the European defence industry. The model resembles some of the key characteristics of the defence sector, and studies how firms in this market will respond to the challenges and opportunities provided by a higher degree of openness and liberalization in the future. The simulation analysis points out that European defence firms will progressively become more efficient, less dependent on public procurement and innovation policy support, and more prone to knowledge sharing and inter-firm collaborations. This firm-level dynamics will in the long-run lead to an increase in the industry’s export propensity and a less concentrated export market.     

Coordinated supply chain management

Historically, the three fundamental stages of the supply chain, procurement, production and distribution, have been managed independently, buffered by large inventories. Increasing competitive pressures, and market globalization are forcing firms to develop supply chains that can quickly respond to customer needs. To remain competitive, these firms must reduce operating costs while continuously improving customer service. With recent advances in communications and information technology, as well as a rapidly growing array of logistics options, firms have an opportunity to reduce operating costs by coordinating the planning of these stages. In this paper, we review the literature addressing coordinated planning between two or more stages of the supply chain, placing particular emphasis on models that would lend themselves to a total supply chain model. Finally, we suggest directions for future research.     

Strategic analysis of technology and capacity investments in the liquefied natural gas industry

Energy plays a fundamental role in both manufacturing and services, and natural gas is rapidly becoming a key energy source worldwide. Facilitating this emergence is an expanding network of ocean-going vessels that enable the matching of natural gas supply and demand on a global scale. This is achieved through the transportation of liquefied natural gas (LNG) for eventual regasification at its destination. Until very recently, only one type of technology had been available for transporting and regasifying LNG: Conventional LNG vessels coupled with land based LNG regasification. But it is now possible to transport and regasify LNG onboard special LNG vessels. Companies such as Excelerate Energy and Höegh LNG are currently developing LNG supply chains based on this new technology. Motivated by these developments, we engaged executives at Excelerate Energy to facilitate an investigation of issues related to strategic technology selection, as well as choices around technology configuration and capacity for the incumbent and emerging technologies. The resulting analysis brings to light managerial principles delineating the impact of alternative LNG throughput models on decisions regarding how to deploy each technology option and how to configure and size their capacity. Our findings have additional potential relevance beyond our industry specific analysis.     

A modeling framework and local search solution methodology for a production-distribution problem with supplier selection and time-aggregated quantity discounts

Supplier selection with quantity discounts has been an active research problem in the literature. In this paper, we focus on a new real-world quantity discounts scheme, where suppliers are selected in the beginning of a strategic planning period (e.g., 5 years). Monthly orders are placed from the selected suppliers, but the quantity discounts are based on the aggregated annual order quantities. We incorporate this type of cost structure in a multi-period, multi-product, multi-echelon supply chain planning problem, and develop a mixed integer linear programming (MIP) model for it. Our model is highly intractable; leading commercial solvers cannot construct high quality feasible solutions for realistic instances even after multiple hours of solution time. We develop an algorithm that constructs an initial feasible solution and a large neighborhood search method that combines two customized iterative algorithms based on MIP-based local search and improves such solution. We report numerical results for a food supply chain application and show the efficiency of using our methodology in getting very high quality primal solutions quickly.     

An integrated optimization model for managing the global value chain of a chemical commodities manufacturer

The realization of supply chain management concepts goes along with the introduction of comprehensive software systems for supporting decisions at the strategic, tactical, and operational planning level. Moreover, in industry the focus has shifted from a pure logistics-oriented view towards the integration of pricing and revenue issues into cross-functional value chain planning models. This paper presents a practical decision support tool for global value chain planning in the production of chemical commodities. The proposed linear optimization model consists of various modules that reflect sales, distribution, production, and procurement activities within a company-internal value chain. The objective of the model is to maximize profit by coordinating all activities within the supply chain. The model formulation is related to a real industry case. It is shown how the model can be used to support decision making from sales to procurement by volume and value.     

A multi-class multi-level capacitated lot sizing model

When demand loading is higher than available capacity, it takes a great deal of effort for a traditional MRP system to obtain a capacity-feasible production plan. Also, the separation of lot sizing decisions and capacity requirement planning makes the setup decisions more difficult. In a practical application, a production planning system should prioritize demands when allocating manufacturing resources. This study proposes a planning model that integrates all MRP computation modules. The model not only includes multi-level capacitated lot sizing problems but also considers multiple demand classes. Each demand class corresponds to a mixed integer programming (MIP) problem. By sequentially solving the MIP problems according to their demand class priorities, this proposed approach allocates finite manufacturing resources and generates feasible production plans. In this paper we experiment with three heuristic search algorithms: (1) tabu search; (2) simulated annealing, and (3) genetic algorithm, to solve the MIP problems. Experimental designs and statistical methods are used to evaluate and analyse the performance of these three algorithms. The results show that tabu search and simulated annealing perform best in the confirmed order demand class and forecast demand class, respectively.     

A robust optimization model for agile and build-to-order supply chain planning under uncertainties

Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms’ success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various kinds of uncertainty. An integrated optimization approach of procurement, production and distribution costs associated with the supply chain members has been taken into account. A robust optimization scenario-based approach is used to absorb the influence of uncertain parameters and variables. The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multi-period, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio and guidance related to future areas of research is given.     

全球化背景下核型创新网络竞争机制研究——基于改进LV-EG模型的仿真分析

在全球化背景下,核型创新网络在向全球创新网络演进的过程中面临多市场竞争和关键资源竞争的双重考验。首先构建全球市场环境下核型创新网络多层次竞合模型;其次,运用Lotka-Volterra模型和演化博弈模型分别剖析核心企业间的竞争关系和尾端企业间的竞合关系,并据此构建改进的LV-EG模型以分析尾端企业竞合关系对核心企业间竞争关系的作用机理;最后,运用matlab对改进的LV-EG模型进行仿真。仿真结果表明:核心企业的尾端企业报酬支付能力、创新产品生产能力、创新环境和网络结构对核型创新网络在全球范围内吸引优质企业并在全球市场环境中获取竞争优势具有重要影响。     

Supplier selection in supplier chain management using Choquet integral-based linguistic operators under fuzzy heterogeneous environment

The fierce competition and the globalization of the market economy urge manufacturing organizations to adopt advanced manufacturing paradigms for sustaining in the global markets. Supply chain management is an essential ingredient of advanced manufacturing systems and finding those partners with the best fit to the existing supply chain is a vital issue concerned in the process of managing supply chains. Due to the decision maker’s knowledge field and the nature of evaluated attributes, assessments are always with different formats, which were first unified into the linguistic terms in the standard linguistic set. Two additive fuzzy measures were used to model criteria interactions by pairs and to derive the special expressions of Marichal entropy and Choquet integral, which is more convenient to use in practice. Fuzzy measures were identified based on the maximum of Marichal entropy. The decision making procedure was illustrated taking an automobile manufacturing industry as an example, compared with the other methods, and showed the feasibilities and advantages. The inadequacies as well as the further research directions were proposed.     

制造业产品质量供给能力及其影响机制研究

在产品质量供需结构性错位背景下,从供给侧出发,本文引入质量内生决定模型測算2005-2018年制造业产品质量供给能力,构建制造业产品质量供给能力影响机制模型。研究表明:质量需求、产品价格、生产率、质量激励政策与质量供给能力呈显著正相关,数量需求、投入品成本和技米创新水平与产品质量供给能力存在负向关系。质量激励政策可以提高制迻业质量供给能力及其稳定性,并存在政策实施的空间延展性和时间衰减性,需要持续改进与优化质量激励政策,保障政策实施的效率和协同性。     

Measurement of a linkage among environmental,operational, and financial performance in Japanese manufacturing firms: A use of Data Envelopment Analysis with strong complementary slackness condition

This study investigates a linkage among environmental, operational and financial performance in Japanese manufacturing industry. All manufacturing firms examined in this study are listed in Tokyo stock exchange market. We use DEA (Data Envelopment Analysis) as an evaluation methodology. This study finds that large firms have managerial capabilities to improve their operational and environmental performance. The improvement leads to the enhancement of their financial performance. However, we cannot find such a business linkage in small and medium-sized firms. They improve their operational performance and then direct themselves toward the improvement of their environmental performance. Their environmental performance is, not the first priority, the second priority for the small and medium-sized firms even though Japanese government is currently making a policy pressure on all manufacturing firms to pay attention to various environmental issues related to the global warming and climate change. The environmental protection policy is effective on only large Japanese manufacturing firms that have technological and financial capabilities for environmental protection.     

Carbon tax based on the emission factor: a bilevel programming approach

We present a bilevel programming approach to design an effective carbon tax scheme based on the production emission factor, used as an intensity measure, for a competitive market with multiple players. At the upper level, the government sets a target emission factor for the industry and taxes firms if they exceed that target. At the lower level, the industry sets output levels that maximize social welfare. The bilevel model is transformed to a linear MIP by replacing the lower level optimization problem by its KKT conditions, and linearizing the complementarity slackness conditions. We test the model in the context of the cement industry. The results show that the proposed model finds the optimal tax rate that induces firms to switch to less carbon-intensive fuels and reduces the overall emissions.     

A lexicographic approach to bi-objective scheduling of single-period orders in make-to-order manufacturing

This paper presents a lexicographic approach and integer programming formulations for a dual-objective, long-term production scheduling in make-to-order manufacturing environment. The problem objective is to assign single-period customer orders for various product types to planning periods to complete all the orders with minimum number of tardy orders as a primary criterion and to level the aggregate production or the total capacity utilization over a planning horizon as a secondary criterion. Each order must be completed during one planning period. The basic integer programming formulation has been strengthened by the addition of some cutting constraints derived by relating the demand on required capacity to available capacity for each subset of orders with the same due date. The approach has been applied to optimize production schedules in a flexible flowshop made up of several processing stages in series, with identical, parallel machines, and an output buffer of limited capacity for holding completed products before delivery to the customers. Numerical examples modeled after a real-world make-to-order flexible assembly line in the electronics industry are provided and some computational results are reported.     

Application of fuzzy linear programming to aggregate production plan of a refinery industry in Taiwan

After the oil market in Taiwan has been opened to private enterprise, the oil refinery industries in Taiwan have not only entered a completely free market; but also faced the challenge from world competition. In such an environment, there exist varieties of uncertain factors, which have caused the failure of traditional production planning models. Therefore, in this study we develop a responsive and flexible production planning system to cope with uncertain manufacturing factors. On the basis of fuzzy set theory, the uncertain demand and cost are clarified, and a fuzzy linear programming model is proposed to find a maximal profit production strategy with degree of satisfaction. The proposed model has been applied to CPC Corporation, Taiwan. The results show that the developed model is able to provide useful information for developing profit-effective oil refinery strategies in an uncertain environment.     

Competitive dynamics and gaming simulation: lessons from a fishing industry simulator

Experiments with a gaming simulator of the fishing industry show that a wide range of firm and industry performance can arise from players’ differing perceptions of the competitive environment. When transferred into the arena of applied strategy development, the results suggest that modellers should give more attention to modelling alternative conceptualizations of strategic intent in the minds of rival business leaders and the firms they create. This new interpretive emphasis should complement the traditional modelling of cross-functional coordination that has been the hallmark of much published work in strategic modelling and simulation. When leaders and firms in the same industry adopt quite different views of the overall system of resources in the industry, it is important to model the heterogeneity of rival firms in order to understand the dynamic performance of the firm and the industry. We propose a modelling approach that captures heterogeneity among the strategic resources that rival firms seek to build and in the operating goals and coordinating processes they use to build them.